The key idea we leverage is that clusters correspond to object parts, and that a set of object parts frequently co-occur together.
Thus, local co-occurrence of clusters in an image should provide a hint about an object's constituent parts.
Using co-occurrence statistics to categorize objects has been proposed before in [1]}, [2]}.
However, we are the first to work with object parts and no labels and employ a novel network science method to discover objects.
To group the clusters, we construct an undirected and weighted co-occurence network \(G = (V, E, w)\) , with \(v_i, i \in \lbrace 1, \dots , K\rbrace \)  corresponding to each cluster.
We use a localized co-occurrence variant that regards the 8-neighborhood up to a pixel distance \(d\) .
Then, we calculate the conditional co-occurrence probability \(P(v_j|v_i)\)  for clusters \(i\)  and \(j\)  over all images \(D\) .
With the co-occurrence probabilities at hand, we define \(w(e_{i,j}) = \min (P(v_j|v_i), P(v_j|v_i))\) .
This asymmetric edge weight definition is motivated by the fact that parts need not be mutually predictive:
For instance, a car windshield might co-occur significantly with sky but presence of a sky is not predictive for a car windshield.
